Visitors to foreign countries are frequently faced with language barriers, amongst other issues. Additionally, even if the visitor is somewhat familiar with the language, use of local or colloquial terms and phrases may still cause confusion and/or difficulty. Vendors such as Babel Fish (Yahoo), Google and a variety of others currently provide utilities that are able to translate text as well as web pages from one language to another. For text translation, for example, a user may specify “from” and “to” languages, and the translator may then start looking for matches on its internal dictionaries. If, and when, a match is found, the translation may be provided back to the user. An overview of this process is illustrated in FIG. 1. Specifically, User 105 may provide a Word and/or Phrase 110 to a translation utility, Trans Utility 115. Upon receipt of Word/Phrase 110, Translation Utility 115 may determine the language Word/Phrase 110 is to be translated to and retrieve Standard Language Dictionary 120 for that specific language. Thereafter Translation Utility 115 may translate Word/Phrase 110 to Translated Word/Phrase 125 and provide the translation back to User 105.
Typical heuristics/factors used for translation today are based on the character set of the text, the number of matches of the words of a sentence on the corresponding language dictionaries, the surrounding words, position in a sentence and/or punctuations. These heuristics are useful but not comprehensive, especially for languages that are used among several countries, regions or cultures, where the same sentence or word can have the same spelling, character set and context but mean completely different things depending on the place where that sentence was used. For example, when translating the word “borde” from Spanish to English, at least two meanings may be correct: “edge” and “arrogant person.” In South America, “borde” is understood only as “edge” but in Spain, on the other hand, “borde” may mean either “edge” or “arrogant person”. If more than one match is found, i.e., a matched word has several possible meanings in the “to” language, existing utilities typically select and present the most common meaning to the user.